Autonomous Vehicles need precise information as to the Drive-able space in order to be able to safely navigate. In recent years deep learning and Semantic Segmentation have attracted intense research. It is a highly advancing and rapidly
evolving field that continues to provide excellent results. Research has shown that deep learning is emerging as a powerful tool in many applications. The aim of this study is to develop a deep learning system to estimate the Free Drive-able space.
Building on the state of the art deep learning techniques, semantic segmentation will be used to replace the need for highly accurate maps, that are expensive to license. Free Drive-able space is defined as the drive-able space on the correct side
of the road, that can be reached without a collision with another road user or pedestrian. A state of the art deep network will be trained with a custom data-set in order to learn complex driving decisions. Motivated by good results, further deep learning techniques will be applied to measure distance from monocular images. The findings demonstrate the power of deep learning techniques in complex driving decisions. The results also indicate the economic and technical feasibility of semantic segmentation over expensive high definition maps.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:72326 |
| Date | 02 October 2020 |
| Creators | Gallagher, Eric |
| Contributors | Hardt, Wolfram, Saleh, Shadi, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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